Land subsidence is a critical issue to be addressed for large cities located near the sea. The monitoring of land subsidence is vital for predicting and managing the disasters that might occur. Many methods have been established to conduct this work, such as using geotechnical monitoring instruments and applying artificial satellite technologies. Those methods can provide highly accurate measurements for small areas. However, it would be expensive and ineffective to apply them to extensive areas. Hence, a monitoring method, that is economical to conduct, can be applied quickly and continuously, and can provide accurate measurements over large areas, is needed. Multi-temporal Differential Interferometry Synthetic Aperture Radar (MT-DInSAR), such as the Small Baseline Subset (SBAS), is a powerful technique for meeting the above demands. And, since the lifespans of current SAR satellites are commonly designed to be around 5–7 years, continuous monitoring for longer periods by the MT-DInSAR technique is important. To deal with these types of issues, a new method is required that can utilize the data from multiple (different) satellites.
In this study, a method for long-term land subsidence monitoring by MT-DInSAR, using multi-sensor data sets, is presented. Firstly, the SBAS method is performed for each time series SAR data set. Secondly, the hyperbolic fitting method is applied to estimate real values from the results of each data set. Finally, the hyperbolic curve is used to connect the results of the unlinked time series data sets. To verify this method, the land subsidence in Semarang City, Indonesia is taken as an example case.
DInSAR is an invaluable tool for observing land surface deformation over vast areas with the high accuracy of centimeter and high-spatial resolution of 3–30 m after spatial averaging and geocoding. Moreover, DInSAR does not require the installation of any devices on the ground, and it has been widely used for detecting horizontal and vertical displacements of the land surface (Hanssen, 2002).